---
license: mit
language:
  - en
tags:
  - multi-agent
  - multimodal
  - strategic reasoning
---

## Dataset Description

- **Homepage:** https://vs-bench.github.io
- **Repository:** https://github.com/zelaix/VS-Bench
- **Paper:** https://arxiv.org/abs/2506.02387
- **Contact:** [Zelai Xu](mailto:zelai.eecs@gmail.com)


### Dataset Summary

VS-Bench is a multimodal benchmark for evaluating VLMs in multi-agent environments. We evaluate fourteen state-of-the-art models in eight vision-grounded environments with two complementary dimensions, including offline evaluation of strategic reasoning by next-action prediction accuracy and online evaluation of decision-making by normalized episode return.


### Citation Information
```
@article{xu2025vs,
  title={VS-Bench: Evaluating VLMs for Strategic Reasoning and Decision-Making in Multi-Agent Environments},
  author={Xu, Zelai and Xu, Zhexuan and Yi, Xiangmin and Yuan, Huining and Chen, Xinlei and Wu, Yi and Yu, Chao and Wang, Yu},
  journal={arXiv preprint arXiv:2506.02387},
  year={2025}
}
```